Automated Obscurity for Digital Imaging

ABSTRACT

Obfuscating a human or other subject in digital media preserves privacy. A user of a smartphone, for example, may enable a flag for obscuring her face in digital photos or movies. When any device captures digital media, the user&#39;s smartphone transmits the flag for receipt. The device capturing the digital media is thus informed of the user&#39;s desire to obscure her face or even entire image. The device capturing the digital media may thus perform an obscuration in response to the flag.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.14/334,157 filed Jul. 17, 2014 and since issued as U.S. Patent X, andincorporated herein by reference in its entirety.

The present disclosure relates generally to communication networks and,more particularly, to systems and methods for obfuscating unwillingsubjects in captured media.

BACKGROUND

Mobile devices, e.g., Smartphones and tablets, have excellentpoint-and-shoot cameras, allowing users to take numerous pictures andvideos any time and everywhere. Furthermore, wearable devices such assmart goggles can record media in even more subtle ways in public andprivate places, with little or no awareness from the subjects in thesurrounding areas captured by the smart goggles. The pervasive use ofthese mobile devices can compromise the privacy of all the individualswho are unaware subjects of these captured pictures and videos, whichcould also be published without explicit consent on the Internet and onsocial media sites.

SUMMARY

In one embodiment, the present disclosure discloses a method forobfuscating an image of a subject in a captured media. For example, themethod receives a communication from an endpoint device of a subjectindicating that the image of the subject is to be obfuscated in acaptured media. The communication may include a feature set associatedwith the subject, where the feature set contains facial features of thesubject and motion information associated with the subject. The methodthen detects the image of the subject in the captured media. Forexample, the image of the subject is detected by matching the facialfeatures of the subject to the image of the subject in the capturedmedia and matching the motion information associated with the subject toa trajectory of the image of the subject in the captured media. Themethod then obfuscates the image of the subject in the captured mediawhen the image of the subject is detected in the captured media.

In another embodiment, the present disclosure discloses an additionalmethod for communicating a feature set. For example, the method recordsmotion information of an endpoint device associated with a subject andtransmits a communication indicating that the image of the subject is tobe obfuscated in the captured media. In one embodiment, thecommunication includes a feature set associated with the subject, wherethe feature set includes facial features of the subject and the motioninformation of the endpoint device.

In still another embodiment, the present disclosure discloses a furthermethod for obfuscating an image of a subject in a captured media. Forexample, the method receives a captured media from a recording endpointdevice and receives a communication from an endpoint device of a subjectindicating that the image of the subject should be obfuscated in acaptured media. The communication may include a feature set associatedwith the subject, where the feature set contains facial features of thesubject and motion information associated with the subject. The methodthen detects the image of the subject in the captured media. Forexample, the image of the subject is detected by matching the facialfeatures of the subject to the image of the subject in the capturedmedia and matching the motion information associated with the subject toa trajectory of the image of the subject in the captured media. Themethod then obfuscates the image of the subject in the captured mediawhen the image of the subject is detected in the captured media.

BRIEF DESCRIPTION OF THE DRAWINGS

The teaching of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an exemplary network related to the presentdisclosure;

FIGS. 2A and 2B illustrate exemplary acceleration data value graphs, inaccordance with the present disclosure;

FIG. 3 illustrates a flowchart of a method for obfuscating an image of asubject in a captured media, in accordance with the present disclosure;

FIG. 4 illustrates a flowchart of another method for communicating afeature set, in accordance with the present disclosure;

FIG. 5 illustrates a flowchart of still another method for obfuscatingan image of a subject in a captured media, in accordance with thepresent disclosure; and

FIG. 6 illustrates a high-level block diagram of a general-purposecomputer suitable for use in performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The present disclosure broadly discloses methods, computer-readablemedia and devices for obfuscating images of subjects in captured media.Examples of the present disclosure are discussed below in the context ofendpoint device-based and/or peer-to-peer based arrangements, e.g.,using any one or more short range communication technologies, such asWi-Fi Direct. Additional examples are also described in the context ofwireless access networks and Internet Protocol (IP) networks. However,it should be noted that specific examples are provided for illustrativepurposes only and that the present disclosure may alternatively oradditionally be applied to packet switched or circuit switched networksin general, e.g., Voice over Internet Protocol (VoIP) networks, Serviceover Internet Protocol (SoIP) networks, Asynchronous Transfer Mode (ATM)networks, Frame Relay networks, various types of cellular networks, andthe like.

Privacy leakage through published digital media is difficult, if notimpossible, to completely control for all appearances in third-party,and sometimes public, photos and videos without having expressedexplicit consent. An individual and his or her children in a crowdedplace, e.g., Times Square of New York City, may become unaware subjectsof hundreds of pictures taken from surrounding strangers. Similarly, apicture captured at a party or a restaurant may be posted on a socialnetwork site with no knowledge of the involved subjects. Whether apersonal preference or the potential for picture misuse, individuals maywish to restrict their appearance in uncontrolled media.

As described herein, embodiments of the present disclosure are sometimesreferred to as Do Not Capture (DNC), a technology that removes unwillingsubjects from captured media. Through a combination of mutualcooperation between mobile devices and image matching algorithms (facedetection and face matching algorithms in particular), DNC effectivelyobfuscates unwilling subjects in captured media with high accuracy. Asused herein, the term “obfuscate” is intended to mean the altering ofmedia such that a subject of the media is not personally identifiable.For example, obfuscation may include blurring an image of a subject suchthat a face or other aspects of a subject cannot be attributed to thesubject. For instance, blurring may comprise altering pixel values of animage of the subject by averaging the values of groups of nearby pixelsso as to pixelate the image. In another example, obfuscation maycomprise blocking out all or a portion of an image of a subject, e.g.,setting pixels to black or to another color, replacing all or a portionof an image of a subject with an image of a different subject, or withanother image, e.g., a non-human image, such as a logo. In still anotherexample, obfuscation may comprise removing the image of the subject froma foreground of the captured media and reconstructing the background.

In one example, DNC may be implemented in a completely distributedmanner, with no need of cloud support, and is designed to efficientlyrun on a mobile device in a scalable manner with little impact on mobiledevice's performance. The DNC pipeline can be partitioned as follows: i)when a person (a “taker”) takes a picture, his or her device collectsface feature vectors from nearby subjects' devices; ii) the taker'sdevice then uses one or more face detection techniques to identify humansubjects in the image; iii) the taker's device next performs a facematching algorithm using the collected face feature vectors to identifypossible DNC subjects in the image (in one embodiment, the face matchingalgorithm is enhanced using motion/orientation information obtained bydevices of the subjects); and iv) finally, the taker's device obfuscatesor blurs any face belonging to detected DNC subjects.

To illustrate, if a person (DNC subject) does not want to be included inpictures taken by strangers, the subject may enable a DNC flag on his orher mobile device which causes the mobile device to wirelessly transmita DNC signal. The DNC signal indicates the subject's desire that his orher image be obfuscated in any photographs or other captured media.Accordingly, in one example a device of a person taking a photo or video(“taker”) performs a periodic scan over a short-range wirelessinterface, e.g., Wi-Fi Direct, Bluetooth of Bluetooth SIG, and so forth,to detect DNC subjects in the area. If a DNC flag being transmitted by asubject's device is detected, the taker's device obtains from thesubject's device a feature vector comprising (1) information regardingthe subject's face, and/or (2) motion/orientation information collectedby the subject's device. In one example, the taker's device thenperforms face and motion/orientation matching with any subjects presentin the picture. In one example, a face is obfuscated (e.g., blurred,rendered unclear, and the like) upon matching a DNC subject's featurevector. By blurring faces at capture time, a subjects' identity isobfuscated before the media is saved on the taker's device or pushed tothe Web. Alternatively or in addition, image in-painting, seam carving,pixel inference and other techniques may be used to obscure an image ofa subject's face (or a larger portion of a subject's body).

Notably, many object recognition algorithms (including facialrecognition algorithms) perform optimally under uniform lighting andstatic conditions, which is not necessarily the case in a DNC setup. Inaddition, most of these techniques are designed to operate on powerfuldesktop machines and not on more resource-constrained mobile devices.Thus, the present disclosure enhances facial recognition techniques withmotion and/or orientation information to improve recognitionperformance.

In one embodiment, it is assumed that each individual will enable anddisable DNC on his or her own device based upon a personal awareness ofthe surroundings and context, e.g., enabling the DNC feature ornotification in public places, disabling the DNC feature or notificationat home or when there are known people around. In one embodiment, it isalso assumed that the underlying software and hardware of a mobile orwearable device is trusted and will not be modified in a malicious way,e.g., with DNC being either rooted inside the device's operating systemor implemented as an application that can be downloaded or otherwiseobtained from a trusted source.

In one example, the present disclosure comprises a system that utilizesa DNC feature vector, which may be constructed from quantized vectors ofa subject's face, and motion/orientation sensor data. In one example,the quantized vectors may comprise eigenvectors of a subject's face(also referred to as an eigenface). In one example, a subjects'eigenface is extracted during a one-time offline training phase.Accordingly, an eigenvector/eigenface-based algorithm is then used todetermine facial similarity. Utilizing this algorithm, a single featurespace (basis) is constructed from a large set of training face dataobtained off-line. Thus, in accordance with the present disclosure, whena photograph or video is taken, all detected faces in the media areprojected into the known basis to generate smaller dimensional features.The system then calculates the Euclidean distance between each of theprojected faces and the eigenface of the subject to determine the bestmatch.

It should be noted that although various embodiments are described inconnection with an eigenface algorithm, the present disclosure is notlimited to the use of this technique. For example, the set of quantizedvectors may be encoded using techniques such as principal componentanalysis (PCA), partial least squares (PLS), sparse coding, vectorquantization (VQ), deep neural network encoding, and so forth. However,while other recognition algorithms may be optimized for variousscenarios, eigenface is a suitable baseline algorithm due totransmission requirements for small dimensional feature vectors, fastbasis projections with linear algebra, and support for simple distancemetrics. In addition, for simplicity, examples are primarily describedherein in connection with capturing of a photograph. However, it shouldbe noted that the present disclosure is broadly applicable tophotographs and video, as well as other audiovisual media in which animage of a person may be captured and for which associated motion datais available.

In one example, the motion/orientation sensor data (broadly andcollectively a “motion signature” or “motion fingerprint”) is obtainedin-real time from motion sensors within or associated with a subject'sdevice. For example, one embodiment may include an accelerometer,gyroscope, and/or magnetometer. In one example, the sensor readingsprovide a motion signature comprising time-series data for a rotationvector and linear acceleration (e.g., in three dimensions). In oneembodiment, an orientation of the subject's device may also bedetermined from the accelerometer, gyroscope, and/or magnetometerreadings. Thus, for purposes of the present disclosure “motioninformation”, or a “motion signature” may also be considered to includeorientation information regarding a subject's device. In any case, withaccess to the motion signature, the system may boost accuracy of theeigenface-based matching algorithm by eliminating obvious true negativesusing orientation information and by motion matching. For example, thereis no need to consider a subject that, based upon the orientationinformation, is determined to be facing in the same direction as thetaker's camera (since the subject is either behind the camera or withhis or her back to the camera). This information is especially usefulbecause the feature dimension from the subject device's magnetometer andgyroscope is very small and retrieving this information does not incursignificant processing cost or delay.

In addition, a taker's device may determine a motion trajectory of asubject in a photograph from the analysis of consecutive frames capturedby the camera of the taker's device before taking the photograph.Accordingly, the taker's device may then compare a DNC subject's motionsignature with a subject's motion trajectory from the analysis ofconsecutive frames, or a sequence of frames, captured by the camera ofthe taker's device before taking a photograph. For example, if a subjectis moving left to right, this action will be captured by both theorientation sensors of the subject's device and by the camera of thetaker's device. As such, the taker's device can either increase ordecrease the confidence of any potential facial matches. In addition,the taker's device may use motion trajectory information obtained by thecamera of the taker's device to reduce the search space within aphotograph. For example, the taker's device may filter out any movingsubject in the photograph that is not posing, and hence not an activeparticipant in the photograph.

To illustrate the foregoing principles, suppose a taker's device detectsK different people in a photograph. M possible subjects, with M≦K, arein proximity of the taker and only N of them, with N≦M, are DNCparticipants. The N individuals send the taker's device (e.g., via Wi-FiDirect) a feature vector containing the eigenfaces and the motion sensordata. The taker's device then performs the matching algorithm for eachof the N individuals, by comparing two components: (1) the facerepresentation/eigenface obtained by the taker from each individual withthe eigenface of each person detected in the photograph, and (2) eachindividual's motion fingerprint from the motion/orientation sensor datawith the motion trajectory extracted by the taker for each individualdetected in the photograph from frame sequence analysis. The problem canbe formulated as follows: with one taker, K detected faces (F₁ . . .F_(K)), N DNC subjects (S₁ . . . S_(N)), and M (N≦M) people, the taker'sdevice computes the Euclidean distance function distance (F_(i), S_(j)).Then, for each face (F_(i)), i=1 . . . K, the algorithm returns apositive match with the smallest distance value as formalized inEquation 1 below:

match_(i)=argmin_(j)[dist(F _(i) , S _(j))] ∀j,j=1 . . . N, φ  Equation1.

In one embodiment, the motion information matching may be used toincrease or decrease a confidence of the result of the facial featuresmatching algorithm. However, in another embodiment the system mayperform an eigenvector-based matching algorithm using the entire featurevector. For example, the feature vector itself may comprise a set ofeigenvectors of the subject's face (e.g., an eigenface), as well as oneor more data points (e.g., additional eigenvectors) from themotion/orientation sensor data.

As mentioned above, communications between taker's and DNC subjects'devices occur over a short-range radio interface. DNC is designed tocover scenarios where a taker and a DNC subject are less than 20 metersapart. Beyond this distance, individuals in photographs tend to becomenon-identifiable. In accordance with the present disclosure variousshort-range wireless technologies may be utilized such as, Bluetooth,Bluetooth Low Energy (BLE), Wi-Fi Direct, and so forth. However, theconventional Bluetooth radio is often considered to be power hungry andmay support only seven slaves. Hence only a limited number of taker andDNC subjects can be accommodated in a distributed/peer-to-peer DNCsystem. BLE may be more desirable in certain situations, when it iswidely supported.

Thus, in one example, Wi-Fi Direct is utilized, and in particular, theWi-Fi Direct Service Discovery (WDSD) feature. In this mode, devicesperiodically broadcast their presence along with some metadata toannounce their service availability. This service is, for example, usedby wireless printers to announce their presence and capabilities. TheWDSD feature is well suited for use in embodiments of the presentdisclosure because of its low-energy footprint and its connection-lessnature, which eases data transport when nodes are mobile (no need tomaintain connections). WDSD allows limited data payload, e.g., about 500bytes on some of the latest smartphones, which in one embodiment isutilized to encode a DNC subject's feature vector for over-the-airtransmission. It should be noted that the precise payload limit may bedevice-specific, and therefore may vary slightly from device to device.In one example, the feature vector, which is a combination of aneigenface and orientation sensor data, is tailored to fit this limitedpayload. For example, a facial feature space of 80 eigenvectors, with 4bytes each, results in eigenface data of 320 bytes, with additionalpayload available for motion signature data. This also minimizes the DNCdata overhead, with little extra power and battery consumption necessaryover the baseline.

In one example, a common face domain is used, e.g., a dedicated facedatabase. To train individual face models, in one embodiment a subjectis requested to capture a video when he or she first beginsparticipating in DNC, e.g., during or after the initial installation ofa DNC application on the subject's device. For instance, the DNCapplication may assist the subject in capturing a video from which aplurality of frames are extracted uniformly and from which faces areprojected onto the common face domain. For example, 20 frames may beextracted and the face in each frame projected into an eigenspace. Inone embodiment, the projected features for each frame are averaged togenerate a single eigenface (face model) that represents the subject,and the model is stored as a persistent file on the device. In oneembodiment, the subject may be requested to engage in different facialexpressions and to change the recording angle to enhance effectivenessof the model and to assist capture of the overall structure of his orher face.

To conserve battery resources, the taker's device may decline to performa matching process for a particular subject if it can be determined thatthe subject is oriented in a direction that faces away from the taker'scamera. For example, a rotation vector and/or orientation sensor readingmay be obtained for both the taker's device and the subject's device anda difference angle determined. In a system where it is assumed that asubject perfectly facing the taker's camera is at an angle of 90 degreesand a backward angle is 270 degrees, any angle greater than 180 degreesis excluded as a possible match. This feature is particularly useful inthe case of wearable devices, where the rotation vector sensor data maybe assumed accurate regarding orientation of the subject's face and theorientation of a camera of the taker's device.

To further conserve resources, in one example a taker's device sets acapture flag (e.g., as a WDSD service object) which informs anylistening nearby devices that the taker's device is about to capture aphotograph. This allows a listening device to begin recordingmotion/orientation data upon receiving the notification. At the sametime, the taker's device may also begin scanning the environment. Forinstance, the taker's device may activate a camera and begin tracingface trajectories. Typically there is between three and ten seconds fromthe time of the activation of the application and the time when thetaker presses a button to capture a scene. After taking the photograph,the taker's device may turn off the capture flag to indicate to nearbylistening devices that the recording is complete.

In turn, a listening device may then choose to send a feature vectorcomprising an individual's facial representation and motion signature tothe taker's device. As described above, the taker's device may thencompare the facial representation with faces detected in the photographand compare the motion signature with motion trajectories detected priorto taking the photograph in order to identify a DNC subject in thephotograph. It should be noted that while the time in which the taker'sdevice and listening device must record motion information is variable,the DNC process may be configured to utilize a fixed data size or mayhave a maximum data size such that less than all of the recorded data issaved. For example, only the last three seconds of data may be utilizedin the event that more than three second of data is recorded.

In one example, orientation and acceleration data are recorded by asubject's device in a three-dimensional world coordinate system. Thetaker's device then uses its own orientation sensor reading to translatethe subject's acceleration values according to the taker's coordinatesystem. The taker's device also monitors the motion trajectory(horizontal and vertical axes) of each face in the field of view of thecamera and compares the motion trajectory against the receivedacceleration values from the subject (projected onto the coordinatesystem of the taker's device).

In one example, the motion trajectory of a face is maintained as aseries of positions in two dimensions of the viewfinder. FIGS. 2A and 2Billustrate two scenarios where acceleration data values in one dimension(as measured by an accelerometer of a subject's device and as measuredby a taker's device via motion trajectory tracking) are plotted againstone another. As shown in the chart 210 of FIG. 2A, the two plots showthat the acceleration data values over time are closely matched. Thus,it is likely that the subject and the face being tracked by the taker'sdevice are the same. However, in the chart 220 in FIG. 2B, the two plotsare highly divergent. Thus, the current subject is likely not the facebeing tracked by the taker's device. In particular, the subject appearsbe engaging in significant movements, while the face being observed bythe taker's device is essentially stationary.

One challenge is matching the trajectory against acceleration values(recorded in m/s²) as measured by the subject's accelerometer(s). In oneexample, properties of a detected face include the height and width inthe viewfinder and the detected face's position. Correlation of theheight and width of a detected face with an average adult head size(e.g., 22.5 cm in height) normalizes the position of a face. In oneembodiment, for each axis, a 4-variable Kalman Filter estimatesposition, velocity, acceleration, and jerk along the axis. Kalman Filteris a popular tool used to estimate variables from noisy observationsover time and have been widely adopted for face or object tracking incomputer vision.

In one embodiment, to handle delays due to the Kalman Filter and delaysin detecting moving faces in the viewfinder, a dynamic time warping(DTW) technique is employed at fixed-size time intervals (e.g., 100 ms)and using an average acceleration value for each corresponding timeinterval. This aggregation is also useful when attempting to distinguishwhether a particular time series represents a static or moving subject.Specifically, low magnitude bins (e.g., magnitude less than 0.1 m/s²)may be counted in a given time and the fraction of such bins computedover the series. If the series of acceleration values shows mostlystatic time bins (e.g., more than 50% static time bins), and anotherseries shows mostly moving (e.g., less than 30% static time bins), thesystem can conclude that the two sets do not match. In other cases, theminimum distance by DTW is obtained and normalized by the number of timebins to use as a motion fingerprint distance metric. A second adjustmentaccounts for the use of approximate head size in face positionnormalization. While the normalization into the same units makes the twosets of acceleration values comparable, a small scaling factor isapplied (e.g., less than 2) based on the peak magnitude to reduce theimpact of approximation on the DTW distance. In one example, toaccommodate a current data transfer limitation on Wi-Fi Direct,acceleration values are aggregated with 200 ms granularity and DTW isapplied to obtain two time series. The minimum DTW distance isnormalized with the number of time bins. In one example, a threshold of0.3 is utilized to distinguish between static and in-motion subjects.

DNC is not intended to completely solve the privacy leakage problem frompervasive imaging as lighting conditions, distance between subjects andtaker, and mobility are all factors that could affect the DNC filteringaccuracy. However, DNC can efficiently operate in many scenarioscommonly encountered during daily experience to protect the identitiesof individuals from uncontrolled media capture in a novel, unique,efficient, and systematic way. While ideally legislation would providethe impetus for adoption of DNC, in the absence of a binding requirementto honor DNC requests, one embodiment incentivizes users to participatein DNC as a taker (one who captures a media content). For example, DNCsubjects may pay a small fee per month, per year or per transaction,where micropayments may be provided to takers who honor DNC requests asan incentive for their willingness to honor such requests.

It is also desirable that no identity reverse engineering should bepossible from a feature vector voluntarily provided by a subject suchthat an attacker could reconstruct a recognizable face. For instance,the greater the number of eigenvectors used, the more recognizable theface. As mentioned above, some embodiments may limit the number ofeigenvectors due to other considerations (e.g., 80 eigenvectors basedupon the size of the WDSD data payload). For example, various facesreconstructed solely from eigenfaces using up to 90 eigenvectors wereobserved and it was determined that although some notable facialstructures are retained, it is impossible to confidently recognize anyindividual.

It should be noted that the foregoing is provided for illustrativepurposes only, and that the present disclosure is not limited to anyspecific implementation described. For example, although the foregoingdescribes examples in connection with a distributed/peer-to-peerarrangement, where local applications on a taker's device and on asubject's device communicate with one another directly, the presentdisclosure may also be embodied in various other arrangements. Forinstance, DNC may be implemented on one or more servers of a serviceprovider, e.g., where a taker's device uploads photograph and motiontrajectory information to a server and the server receives or storesfacial imaging data and motion data from one or more subjects. Usingsuch information, the server may then scan for identifiable subjects inthe photograph. Alternative embodiments of this nature are described ingreater detail below in connection with the discussion of FIG. 1.Similarly, an individual's facial features may be stored and transmittedby a device of a caregiver (e.g., parent) or by a beacon device (e.g.,for a school). In this case, multiple DNC subjects may be protected by asingle device. Protection may also be localized to a particularlocation, e.g., at the school, while additional protection off schoolgrounds and during non-school hours may be left to the decision of aparent/guardian.

In another example, the obfuscation of a subject may comprise encryptingthe portion of the media containing the image the subject with apublic/private key set. Thus, the image of the subject may be capturedand obfuscated with the public key, e.g., provided to the device of thetaker by the device of the subject. At a later date, the image of thesubject could then be unlocked with the appropriate subject-generatedprivate key.

In another example, a voice signature of a subject may be provided inaddition to the subject's facial features and/or motion signature.Accordingly, when recording a video, if the taker's device is capable ofattributing a recorded sound to a particular location (and hence aparticular subject) within the field of view, and is capable of voicematching the recorded voice to the voice signature, the taker's devicemay also remove, distort or otherwise disguise the subject's voice inaddition to obscuring the subject's face. In addition, the voicematching may also be used to enhance the accuracy or confidence of thematching algorithm based upon the facial features. Numerous othervariations of these examples all fall within the scope of the presentdisclosure.

To better understand the present disclosure, FIG. 1 illustrates ingreater detail an exemplary system 100 for obfuscating images ofsubjects in a captured media and/or for forwarding a feature set,according to the present disclosure. As shown in FIG. 1, the system 100connects endpoint devices 170, 171 and 172 with one or more applicationservers 120, 125 via a core Internet Protocol (IP) network 110, acellular access network 140, an access network 150 and/or Internet 180.

In one embodiment, access network 150 may comprise a non-cellular accessnetwork such as a wireless local area network (WLAN) and/or an IEEE802.11 network having a wireless access point 155, a “wired” accessnetwork, e.g., a local area network (LAN), an enterprise network, ametropolitan area network (MAN), a digital subscriber line (DSL)network, a cable network, and so forth, or a hybrid network. As such,endpoint devices 170, 171 and/or 172 may each comprise a mobile device,e.g., a cellular device and/or a non-cellular wireless device, a devicefor wired communication, and so forth. For example, endpoint devices170, 171 and 172 may each comprise one of: a mobile phone, a smartphone, an email device, a computer tablet, a messaging device, aPersonal Digital Assistant (PDA), a personal computer, a laptopcomputer, a Wi-Fi device, a tablet and so forth. In one embodiment,endpoint devices 170, 171 and 172 may include components which supportpeer-to-peer and/or short range communications, e.g., Bluetooth, BLE,Wi-Fi Direct, and the like. In addition, in one embodiment, one or moreof endpoint devices 170, 171 and/or 172 are equipped with digitalcameras, video capture devices and/or microphones in order to supportvarious functions described herein. For example, one or more of endpointdevices 170, 171 and/or 172 may comprise a wearable device, such as ahead-mounted smart camera, a smart watch, a vehicle-mounted smart cameraor the like, an in-place device, such as a security camera mounted on abuilding, a dashboard camera on a car, and so forth.

In one embodiment, cellular access network 140 may comprise a radioaccess network implementing such technologies as: global system formobile communication (GSM), e.g., a base station subsystem (BSS), orIS-95, a universal mobile telecommunications system (UMTS) networkemploying wideband code division multiple access (WCDMA), or a CDMA3000network, among others. In other words, cellular access network 140 maycomprise an access network in accordance with any “second generation”(2G), “third generation” (3G), “fourth generation” (4G), Long TermEvolution (LTE) or any other yet to be developed futurewireless/cellular network technology. While the present disclosure isnot limited to any particular type of wireless access network, in theillustrative embodiment, wireless access network 140 is shown as a UMTSterrestrial radio access network (UTRAN) subsystem. Thus, an element 145may comprise a Node B or evolved Node B (eNodeB).

In one embodiment, core IP network 110 comprises, at a minimum, networkdevices or components which are capable of routing and forwarding IPpackets between different hosts over the network. However, in oneembodiment, the components of core IP network 110 may have additionalfunctions, e.g., for functioning as a public land mobile network(PLMN)-General Packet Radio Service (GPRS) core network, for provingVoice over Internet Protocol (VoIP), Service over Internet Protocol(SoIP), and so forth, and/or may utilize various different technologies,e.g., Asynchronous Transfer Mode (ATM), Frame Relay, multi-protocollabel switching (MPLS), and so forth. Thus, it should be noted thatalthough core IP network 110 is described as an Internet Protocolnetwork, this does not imply that the functions are limited to IPfunctions, or that the functions are limited to any particular networklayer.

FIG. 1 also illustrates a number of individuals at a public location,e.g., in a museum. For example, users 160 and 164 may be friends whohave travelled to the museum together, while users 161, 162 and 163 maybe unknown to each other and to users 160 and 164. As also illustratedin FIG. 1, user 160 may take a photograph 190 using a camera of device170, which captures images of users 161-164 as subjects of the images.

In the present example, all or some of the users 160-164 may be DNCparticipants. For example, users 161 and 162 may set DNC flags on theirrespective devices 171 and 172 indicating that they would like images oftheir faces obfuscated in any captured media. User 160 may alsoparticipate in DNC as a “taker” such that his or her device 170 isconfigured to notify other DNC devices of an intention to capture media,to listen for any DNC flags or notifications from nearby devices and toreceive feature vectors from such nearby devices. Continuing with thepresent example, device 170 may wirelessly send a communicationindicating an intention to capture a picture or video, e.g., picture190. For example, in one embodiment, devices 170, 171 and 172 maycommunicate with one another via Wi-Fi Direct or other short rangecommunication mode. In another embodiment, devices 170, 171 and 172 maycommunicate with one another via one or more network infrastructureelements. For example, access network 150 may comprise a public WLAN, orWi-Fi hotspot, where devices 170, 171 and 172 communicate via wirelessaccess point 155. In response, devices 171 and 172 may begin recordingmotion/orientation sensor data. At the same time, device 170 may trackmovement of any detected faces and/or detected participants in a fieldof view of the camera. After the photograph 190 is captured, device 170may send an indication that the capturing of the media is complete.Accordingly, devices 171 and 172 may send a feature vector with facialfeatures and a motion signature to device 170.

Using all of the information collected, device 170 may then attempt tomatch users 161 and 162 to participants in the photograph 190. Forexample, device 170 may detect the face of user 162 in the photograph190 using the facial features and motion signature from the receivedfeature vector. As such, device 170 may obfuscate the face of user 162in photograph 190 prior to recording the photograph 190 to the deviceand/or uploading to the Web. However, the face of user 161 may not bedetected since user 161 is facing away from the camera in photograph190. User 164 is a friend of the user 160 and was the intended subjectof the photograph 190. Therefore, his or her face is not obfuscated. Inaddition, while user 163 may not be known to user 160, who is taking thephotograph 190, he or she is not a DNC participant. Therefore, the faceof user 163 is also not obfuscated in photograph 190.

Although the foregoing example describes a process that is performed byor on one or more of endpoint devices 170-172, in another embodiment thepresent disclosure is implemented wholly or partially by a network-basedapplication server, e.g., one of application servers 120 or 125. Forexample, photograph 190 may be captured on endpoint device 170 of user160 and uploaded to application server (AS) 125. AS 125 may receive thephotograph 190 in addition to motion trajectory information of varioussubjects in the photograph 190 or a short video clip from a time justprior to the capturing of the photograph 190. In one example, locationinformation of device 170 may also be provided. For example, endpointdevice 170 may reveal its location to AS 125 via GPS coordinates,serving cellular base station identity (e.g., base station 145, IPaddress, and so forth. In one example, device 170 may transmitphotograph 190, motion trajectory information of subjects in thephotograph 190 and location information of device 170 to AS 125 via anyone or more of core IP network 110, cellular access network 140, accessnetwork 150 and/or Internet 180.

In addition, AS 125 may store in DB 126 facial features (eigenfaces) ofone or more users, including users 161 and 162. AS 125 may also receivemotion signature information as well as current location information fordevices 171 and 172 of users 161 and 162 respectively, e.g., via any oneor more of core IP network 110, cellular access network 140, accessnetwork 150 and/or Internet 180. Thereafter, AS 125 may identify DNCsubjects in photograph 190 using the processes described above. Thus, AS125 may similarly detect the face of user 162 in photograph 190 and useone or more obscuring techniques to render the face of user 162unrecognizable in the photograph 190. AS 125 may return photograph 190in a modified form to device 170, store modified photograph 190 in theDB 126 and/or forward modified photograph 190 to another storagelocation or another entity (e.g., a server of a social media provider, acloud storage provider and the like). To reduce the search space, in oneembodiment AS 125 may only consider users/devices who are nearby to thecapturing device and which have a DNC flag activated. In anotherembodiment, the taker's device (e.g., device 170) sends a notificationof an intention to capture media, where the notification has a uniqueidentifier (ID). Device 170 may also provide the unique ID to AS 125. Alistening device nearby may receive the unique ID over-the-air andprovide the unique ID to AS 125. Consequently, AS 125 may only considernearby DNC participants based upon the unique ID. The present disclosuremay also be implemented by AS 120 and DB 121, where AS 120 is operatedby a telecommunications network service provider that may own and/oroperate core IP network 110 and/or cellular access network 140.

In one embodiment, the present disclosure may supplement facialrecognition techniques by identifying a body shape of a participantand/or by identifying articles of clothing, e.g., where there is accessto prior photographs from the same day and where a subject may bewearing the same distinctive outfit. In addition, the above examples aredescribed in connection with sharing of a photograph 190. However, thepresent disclosure is not limited to the context of photographs, butrather encompasses various forms of media content, e.g., videorecordings (with or without accompanying audio). Thus, in oneembodiment, identification of a DNC subject may be enhanced by usingvoice matching techniques in addition to facial feature matching andmotion signature matching. For example, users 161 and 162 may furtherprovide voice feature vectors which provide enough detail to be matchedto voices in an audio portion of a video, but which are not rich enoughto personally identify the subjects.

FIG. 3 illustrates a flowchart of a method 300 for obfuscating an imageof a subject in a captured media. In one embodiment, steps, functionsand/or operations of the method 300 may be performed by an endpointdevice, such as endpoint device 170 in FIG. 1. In one embodiment, thesteps, functions, or operations of method 300 may be performed by acomputing device or system 600, and/or processor 602 as described inconnection with FIG. 6 below. The method begins in step 305 and proceedsto optional step 310.

At optional step 310, the method 300 sends a communication/signalindicating an intent to record captured media. For example, a user(e.g., a taker) of a mobile endpoint device may be a participant in aDo-Not-Capture (DNC) system, where the user's mobile endpoint device maybe configured to transmit a DNC intent-to-record communication to nearbylistening device when the user activates a particular key, access acamera function, and so forth. In one example, the communication may bebroadcast using Wi-Fi Direct, or other short-range wirelesscommunication mode.

At step 320, the method 300 records the captured media. For example, adevice may record a photograph or video (with or without audio) using acamera and/or microphone of the device, or connected to the device.Notably, the media content may include the images of one or moresubjects, any of which may desire that his or her image (face) beobfuscated in the captured media. For example, an individual may take aphotograph or video of a friend, but may inevitably capture the facialimages of various strangers, some or all of whom would prefer to notappear in the media. In one embodiment, prior to or at the same time asthe method 300 records the captured media at step 320, the method 300may further track and gather data regarding motion trajectories offaces/subjects detected by a camera. For example, the method 300 maydetect and track movements of faces, or all or a portion of a body, in afield of view of the camera for a short time (e.g., approximately threeto ten seconds) prior to recording a photograph or video.

At optional step 330, the method 300 sends a communication indicatingthat the captured media is finished being recorded. For example, themethod may stop transmitting an intent-to-capture signal such thatnearby listening devices are made aware that the recording of thecaptured media is complete. In another embodiment, the method 300 maysend a new signal that simply indicates that the media capture iscomplete.

At step 340 the method 300 receives a communication from a mobileendpoint device of a subject indicating that the image of the subjectshould be obfuscated in the captured media. For example, a subjectparticipating in a DNC system may have a mobile endpoint device that isconfigured to listen for DNC communications indicating an intention torecord captured media. In response to detecting such a communication,the listening device may then record orientation/motion information tobe provided after the media is captured by the taker's mobile endpointdevice. Accordingly, in one embodiment the communication may include afeature set, or feature vector associated with the subject that includesa representation of a face of the subject and/or motion information, ora motion signature, associated with the subject. For example, the motioninformation may include acceleration vectors and rotation vectorsrecorded by the mobile endpoint device of the subject in response toreceiving the communication/notification sent at step 310. In oneembodiment, the communication is received wirelessly, e.g., using Wi-FiDirect or other near-field communication technique. In one embodiment,the communication may further include a public key of a public/privatekey pair generated by the device of the subject or otherwise under thecontrol of the subject.

At step 350, the method 300 detects the image of the subject in thecaptured media. For example, the method 300 may perform a matchingprocess as described above to determine a match, or lack of a match to afacial image detected in the captured media. In one embodiment, themethod 300 detects all faces in the image using a facial detectionalgorithm and then attempts to match the facial features of the subjectreceived at step 340 with facial features of each of the detected facesin the image. To enhance the accuracy of the matching, the method 300may further match the motion information of the subject withtrajectories of the facial images detected in the media. For example, asmentioned above, the method 300 may record motion trajectories forfaces/subjects detected in the field of view of a camera. Accordingly,if the motion information does not match a motion trajectory, this mayassist the method 300 in confirming that the subject and a particularfacial image are not a match.

It should be noted that in one embodiment, the method 300 may notattempt to match the subject to a facial image in the media if theorientation information received from the device of the subjectindicates that the subject was facing away from the camera at the timethe captured media was recorded. However, for illustrative purposes itis assumed that this is not the case. In other words, it is assumed thatthe subject matches one of the images in the captured media (e.g., bydetermining the Euclidean distance between a projected face from thecaptured media and the facial features of the subject to determinewhether a match score exceeds a threshold confidence value, enhanced bymatching a motion trajectory with the motion information received fromthe subject's device).

At step 360, the method 300 obfuscates the image of the subject in thecaptured media. For example, the method 300 may blur the image of theface of the subject to protect the subject's identity before thecaptured media is saved or pushed to the Web. Alternatively or inaddition, method 300 may use image in-painting, seam carving, pixelinference and other techniques to obscure the image of the subject'sface (or a larger portion of a subject's body). In one example, theobfuscation incorporates an encryption of the image of the subject usinga public key received at step 340.

At optional step 370, the method 300 may receive a further communicationrequesting or authorizing that the image of the subject beun-obfuscated. For example, the subject may later decide that he or shewould like the image of the subject to be un-obfuscated, for variousreasons. For instance, if the captured media is of a newsworthy event,the subject may change his or her mind as to whether to make public hisor her presence at the event. Thus, in one example, if the image of thesubject is obfuscated using a public encryption key, the communicationreceived at step 370 may include a private key which will enable theobfuscation of the subject's image to be undone.

At optional step 380, the method 300 un-obfuscates the image of thesubject in response to the communication received at step 370. Forinstance, the image may be un-obfuscated using a private key received atstep 370.

Following step 360 or step 380, the method 300 proceeds to step 395where the method ends. Notably, steps 340-380 may be repeated withrespect to a plurality of subjects from which a feature set, or featurevector is received.

FIG. 4 illustrates a flowchart of another method 400 for communicating afeature set. In one embodiment, steps, functions and/or operations ofthe method 400 may be performed by a mobile endpoint device, such asmobile endpoint device 171 or 172 in FIG. 1. In one embodiment, thesteps, functions, or operations of method 400 may be performed by acomputing device or system 600, and/or processor 602 as described inconnection with FIG. 6 below. The method begins in step 405 and proceedsto optional step 410.

At optional step 410, the method 400 receives a notification of anintention to record a captured media. For example, a user (subject) maybe a participant in a do-not-capture (DNC) system, where the subject'smobile endpoint device may be configured to listen for communicationsfrom other nearby mobile endpoint devices indicating an intention torecord a captured media. In one example, the communication may bebroadcast using Wi-Fi Direct, or other short-range wirelesscommunication mode. Thus, the method 400 may listen for thiscommunication/signal and receive the notification at step 410 when thenotification is sent by the nearby mobile endpoint device.

At step 420, the method 400 records motion information. For example, themethod may record a time series of acceleration vectors and rotationvectors of a mobile endpoint device of the subject in response toreceiving the communication sent at step 410.

At optional step 430, the method 400 receives a communication indicatingthat the captured media has been recorded. For example, the mobileendpoint device of the user recording the captured media may stoptransmitting an intent-to-capture signal such that nearby listeningdevices are made aware that the recording of the captured media iscomplete. Thus, the method 400 may listen for and receive thiscommunication at step 430. In one embodiment, the method 400 stopsrecording the motion information when the communication is received atstep 430 indicating that the captured media has been recorded.

At step 440, the method 400 transmits a communication indicating thatthe image of the subject should be obfuscated in the captured media. Forexample, the method may assemble and send a communication that includesa feature set, or feature vector associated with the subject. In oneembodiment, the feature set may include a representation of a face ofthe subject and all or a portion the motion information recorded at step420 (a motion signature). In one embodiment, the communication mayfurther include a public key for use in obfuscating the image of thesubject. In one embodiment, the communication is sent wirelessly, e.g.,using short-range communication techniques such as Bluetooth, ZigBee,Wi-Fi, and so forth.

At optional step 450, the method 400 may transmit a communicationrequesting or granting permission to un-obfuscate the image of thesubject. For example, if the image of the subject is obfuscated using apublic encryption key, the communication sent at step 450 may include aprivate key which will enable the obfuscation of the subject's image tobe undone.

Following step 440 or 450, the method 400 proceeds to step 495 where themethod ends.

FIG. 5 illustrates a flowchart of another method 500 for obfuscating animage of a subject in a captured media. In one embodiment, steps,functions and/or operations of the method 500 may be performed by anetwork-based device, e.g., application server 120 or 125 of a serviceprovider in FIG. 1. In one embodiment, the steps, functions, oroperations of method 500 may be performed by a computing device orsystem 600, and/or processor 602 as described in connection with FIG. 6below. The method begins in step 505 and proceeds to step 510.

At step 510, the method 500 receives a captured media from a recordingdevice, e.g., wearable smart device, a smartphone with integratedcamera, a mobile device with an integrated camera, or coupled(wirelessly or otherwise) to an associated camera. In one example, thecaptured media is received via one or more communication networks, suchas cellular access network 140, core IP network 110, interne 180, and soforth, as illustrated in FIG. 1. For example, the method 500 may receivethe captured media from a mobile endpoint device of a participant in aDo-Not-Capture (DNC) system for purposes of sanitizing the imagescontained in the captured media.

In one embodiment, at step 510 the method 500 further receives locationinformation of the mobile endpoint device and motion trajectoryinformation pertaining to one or more facial images/subjects detected inthe captured media. For example, the recording device (taker device) maydetect and track movements of faces, or all or a portion of a body, in afield of view of the camera for a short time (e.g., approximately threeto ten seconds) prior to taking a photograph, or throughout the durationof a video (in the case where the captured media comprises a video).Thus, the method 500 may receive from the recording device, motiontrajectories for the one or more subjects in the captured media.However, in another embodiment the method 500 may receive a video clipfrom a short period of time prior to the capturing of the photograph (ormay simply receive the video if the captured media comprises a video).In this case, the method 500 may calculate motion trajectories in thesame or similar manner as described above in connection with step 320 ofthe method 300.

At step 520, the method 500 receives a communication from a mobileendpoint device of a subject indicating that the image of the subjectshould be obfuscated in the captured media. For example, a subjectparticipating in a DNC system may have a mobile device that isconfigured to listen for DNC communications indicating an intention torecord captured media. In response to detecting such a communication,the listening device may then record orientation/motion information tobe provided after the media is captured by the taker's device.Accordingly, in one embodiment the communication includes a feature set,or feature vector associated with the subject that includes arepresentation of a face of the subject and motion information, or amotion signature, associated with the subject. For example, the motioninformation may include acceleration vectors and/or rotation vectors ascalculated and/or recorded by the device of the subject in response toreceiving from a nearby device a notification of an intention to recordthe captured media. In addition, in one embodiment the communicationreceived at step 520 may also include location information of thesubject's mobile endpoint device, such that the method 500 may correlatethe captured media with potential subjects in the captured media. In oneexample, the communication is received via one or more communicationnetworks, such as cellular access network 140, core IP network 110,Internet 180, and so forth, as illustrated in FIG. 1.

At step 530, the method 500 detects the image of the subject in thecaptured media. For example, the method 500 may perform a matchingprocess as described above to determine a match, or lack of a match to afacial image detected in the captured media. In one embodiment, themethod 500 detects all faces in the image using a facial detectionalgorithm and then attempts to match the facial features of the subjectreceived at step 520 with facial features of each of the detected facesin the image. To enhance the accuracy of the matching, the method 500may further match the motion information of the subject withtrajectories of the facial images detected in the media. Notably, step530 may involve the same or similar functions/operations described inconnection with step 350 of the method 300 above.

In addition, in one embodiment the method 500 may match the location inwhich the captured media has been recorded with a location of the mobileendpoint device of the subject. In other words, in one example themethod 500 will only scan the captured media to determine if there is amatch to the subject if the subject is nearby to device which recordedand uploaded the captured media (i.e., within a threshold distance, suchas less than 20 meters, less than 50 meters, and so forth).

At step 540, the method 500 obfuscates the image of the subject in thecaptured media. For example, the method 500 may blur the image of theface of the subject to protect the subject's identity before thecaptured media is saved or pushed to the Web. Alternatively or inaddition, method 500 may use image in-painting, seam carving, pixelinference and other techniques to obscure the image of the subject'sface (or a larger portion of a subject's body). Notably, step 540 mayinvolve the same or similar functions/operations described in connectionwith step 350 of the method 300 above. Following step 540, the methodmay proceed to step 595 where the method ends, or may proceed tooptional step 550.

At optional step 550, the method 500 may store or send the capturedmedia that has been modified at step 540. For example, in one embodimentthe method 500 may be executed at a server of a social network or cloudstorage provider which may host the captured media on behalf of theuploading user. However, in another embodiment step 550 may comprisesending the captured media that has been modified back to the uploadinguser, e.g., in an email, multimedia messaging service (MMS) message, orthe like.

At optional step 560, the method 500 may receive a further communicationrequesting or authorizing that the image of the subject beun-obfuscated. Thus, in one example, if the image of the subject isobfuscated using a public encryption key, the communication received atstep 560 may include a private key which will enable the obfuscation ofthe subject's image to be undone.

At optional step 570, the method 500 un-obfuscates the image of thesubject in response to the communication received at step 560. Forinstance, the image may be un-obfuscated using a private key received atstep 560.

Following step 540, step 550 or step 570, the method 500 proceeds tostep 595 where the method ends.

It should be noted that although not specifically specified, one or moresteps, functions or operations of the respective methods 300, 400 and/or500 may include a storing, displaying and/or outputting step as requiredfor a particular application. In other words, any data, records, fields,and/or intermediate results discussed in the respective methods can bestored, displayed and/or outputted to another device as required for aparticular application. Furthermore, steps or blocks in FIGS. 3-5 thatrecite a determining operation or involve a decision do not necessarilyrequire that both branches of the determining operation be practiced. Inother words, one of the branches of the determining operation can bedeemed as an optional step.

FIG. 6 depicts a high-level block diagram of a general-purpose computeror system suitable for use in performing the functions described herein.For example, any one or more components or devices illustrated in FIG. 1or described in connection with the methods 300, 400 or 500 may beimplemented as the system 600. As depicted in FIG. 6, the system 600comprises a hardware processor element 602 (e.g., a microprocessor, acentral processing unit (CPU) and the like), a memory 604, (e.g., randomaccess memory (RAM), read only memory (ROM), a disk drive, an opticaldrive, a magnetic drive, and/or a Universal Serial Bus (USB) drive), amodule 605 for obfuscating an image of a subject in a captured mediaand/or for communicating a feature set, and various input/output devices606, e.g., a camera, a video camera, storage devices, including but notlimited to, a tape drive, a floppy drive, a hard disk drive or a compactdisk drive, a receiver, a transmitter, a speaker, a display, a speechsynthesizer, an output port, and a user input device (such as akeyboard, a keypad, a mouse, and the like).

Although only one processor element is shown, it should be noted thatthe general-purpose computer may employ a plurality of processorelements. Furthermore, although only one general-purpose computer isshown in the Figure, if the method(s) as discussed above is implementedin a distributed or parallel manner for a particular illustrativeexample, i.e., the steps of the above method(s) or the entire method(s)are implemented across multiple or parallel general-purpose computers,then the general-purpose computer of this Figure is intended torepresent each of those multiple general-purpose computers. Furthermore,one or more hardware processors can be utilized in supporting avirtualized or shared computing environment. The virtualized computingenvironment may support one or more virtual machines representingcomputers, servers, or other computing devices. In such virtualizedvirtual machines, hardware components such as hardware processors andcomputer-readable storage devices may be virtualized or logicallyrepresented.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable logicarray (PLA), including a field-programmable gate array (FPGA), or astate machine deployed on a hardware device, a general purpose computeror any other hardware equivalents, e.g., computer readable instructionspertaining to the method(s) discussed above can be used to configure ahardware processor to perform the steps, functions and/or operations ofthe above disclosed methods. In one embodiment, instructions and datafor the present module or process 605 for obfuscating an image of asubject in a captured media and/or for communicating a feature set(e.g., a software program comprising computer-executable instructions)can be loaded into memory 604 and executed by hardware processor element602 to implement the steps, functions or operations as discussed abovein connection with the exemplary methods 300-500. Furthermore, when ahardware processor executes instructions to perform “operations”, thiscould include the hardware processor performing the operations directlyand/or facilitating, directing, or cooperating with another hardwaredevice or component (e.g., a co-processor and the like) to perform theoperations.

The processor executing the computer readable or software instructionsrelating to the above described method(s) can be perceived as aprogrammed processor or a specialized processor. As such, the presentmodule 605 for obfuscating an image of a subject in a captured mediaand/or for communicating a feature set (including associated datastructures) of the present disclosure can be stored on a tangible orphysical (broadly non-transitory) computer-readable storage device ormedium, e.g., volatile memory, non-volatile memory, ROM memory, RAMmemory, magnetic or optical drive, device or diskette and the like. Morespecifically, the computer-readable storage device may comprise anyphysical devices that provide the ability to store information such asdata and/or instructions to be accessed by a processor or a computingdevice such as a computer or an application server.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

1. A method, comprising: capturing, by a mobile device, a digital media representing an image of a subject; receiving, by the mobile device, a mobile-to-mobile wireless transmission sent from a different mobile device, the mobile-to-mobile wireless transmission instructing the mobile device to obfuscate the subject in the digital media; and obfuscating, by a processor operating in the mobile device capturing the digital media, the image of the subject in response to the mobile-to-mobile wireless transmission.
 2. The method of claim 1, further comprising receiving a do not capture flag sent from the different mobile device.
 3. The method of claim 1, further comprising determining the mobile-to-mobile wireless transmission sent from the different mobile device specifies a do not capture flag for the obfuscating of the image of the subject.
 4. The method of claim 1, further comprising determining an enablement of a do not capture flag for the obfuscating of the image of the subject.
 5. The method of claim 1, further comprising determining a facial portion associated with the subject.
 6. The method of claim 5, further comprising blurring the facial portion associated with the subject captured in the digital media. The method of claim 1, further comprising associating the different mobile device to the subject to be obfuscated in the digital media.
 8. A system, comprising: a hardware processor; and a memory device, the memory device storing instructions, the instructions when executed causing the hardware processor to perform operations, the operations comprising: capturing a digital media by a mobile device, the digital media representing an image of a subject; receiving a mobile-to-mobile wireless transmission broadcast by a different mobile device, the mobile-to-mobile wireless transmission specifying an instruction to obfuscate the subject in the digital media; and obfuscating, by the hardware processor operating in the mobile device capturing the digital media, the image of the subject in response to the mobile-to-mobile wireless transmission.
 9. The system of claim 8, wherein the operations further comprise receiving a do not capture flag sent from the different mobile device.
 10. The system of claim 8, wherein the operations further comprise determining the mobile-to-mobile wireless transmission specifies a do not capture flag for the obfuscating of the image of the subject.
 11. The system of claim 8, wherein the operations further comprise determining an enablement of a do not capture flag for the obfuscating of the image of the subject.
 12. The system of claim 8, wherein the operations further comprise determining a facial portion associated with the subject.
 13. The system of claim 12, wherein the operations further comprise blurring the facial portion associated with the subject captured in the digital media.
 14. The system of claim 8, wherein the operations further comprise associating the different mobile device to the subject to be obfuscated in the digital media.
 15. A memory device storing instructions that when executed cause a hardware processor to perform operations, the operations comprising: capturing a digital media by a mobile device, the digital media representing an image of a subject; receiving a mobile-to-mobile wireless transmission broadcast by a different mobile device, the mobile-to-mobile wireless transmission specifying an instruction to obfuscate the subject in the digital media; and obfuscating, by the hardware processor operating in the mobile device capturing the digital media, the image of the subject in response to the mobile-to-mobile wireless transmission.
 16. The memory device of claim 15, wherein the operations further comprise receiving a do not capture flag sent from the different mobile device.
 17. The memory device of claim 15, wherein the operations further comprise determining the mobile-to-mobile wireless transmission specifies a do not capture flag for the obfuscating of the image of the subject.
 18. The memory device of claim 15, wherein the operations further comprise determining an enablement of a do not capture flag for the obfuscating of the image of the subject.
 19. The memory device of claim 15, wherein the operations further comprise determining a facial portion associated with the subject.
 20. The memory device of claim 19, wherein the operations further comprise blurring the facial portion associated with the subject captured in the digital media. 